Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease

Figures

• 

  Figure 1. Overview of our current understanding of Non-alcoholic fatty liver disease (NAFLD) progression from a healthy liver to hepatocellular carcinoma (HCC). The red items indicate the different potential contributors towards NAFLD as well the progression from a healthy liver to HCC. The green items indicate potential disease reversibility from non-alcoholic steatohepatitis (NASH) to a healthy liver, through different methods such as exercise and better glucose control. The blue items showcase the various tests that can be utilized to investigate liver health. The black items represent the omics approaches and use of network modeling to address various knowledge gaps for NAFLD.

• 

  Figure 2. Overview of the methodological workflow in applying network approaches to study NAFLD. Step 1. Through collecting various tissues from multiple species across multiple disease backgrounds, we can run different omics analysis to provide a useful but one-dimension view of what is occurring in the disease state. However, by combining multiple omics datasets, we can provide a holistic picture. Step 2. We can build networks through various calculations of connectivity and regulatory information to elucidate hub genes in disease networks. Step 3. Once networks are built, we can utilize them to address the gaps in disease understanding and therapeutic discovery.

• 

  Figure 3. Summary of findings from network-based studies to elucidate NAFLD progression, mechanisms, comorbidities, sex differences, biomarkers and druggable targets.